We visualize the computation process of the co-visibility mask in Figure REF .
Concretely, for each (a) test frame, we first check its (b) occlusion in each training frame by the forward-backward flow consistency check according to Equation REF .
We use RAFT [1]} for optical flow estimation between each test frame and each training frame.
Note that we visualize occlusion as both a binary mask and its overlay on the test image.
For occlusion mask visualization, the black color indicates pixels with no correspondence in the training views.
We then compute the (c) co-visibility heatmap by simply summing up all test view occlusion masks.
This co-visibility heatmap stores the number of times that each pixel is seen in training views.
For visualization purpose, we normalize the heatmap by the number of the training frames \(N\) .
Finally, we apply a threshold \(\beta \)  to the heatmap and obtain a (d) binary co-visibility mask, which we also visualize with (e) its overlay on the test image.
We adopt a conservative strategy and set \(\beta = \max (5, 0.1\cdot N)\) , meaning that we deem a pixel “seen” during training and valid for evaluation when it is seen in 5 or \(10\%\)  of training frames, whichever is larger.
This strategy ensures high recall in the masking result, i.e., the final co-visible regions are adequately seen during training when the flow estimation is noisy.
For example, as shown in the third row of Figure REF  (b), the test view occlusions are inaccurate and miss the red cover of the chicken toy when it is visible in both frames.
However, since the red cover is adequately seen over the whole sequence, it is still included in the final co-visibility mask.
